Semantic context layer that helps AI agents understand your business data accurately
Stage
Not given
MVP
Founded
Not given
Not given
Based in
Not given
Not given
Pricing model
Freemium
Not given
Pricing
RailCall local runtime: free. RailHub Team: $5 per seat per month. Cloud agents: $5 per trained agent per month. Advanced thinking (overflow): metered against a budget you set, with 500 flows per month included. Storage on hub: $0.25 per GB-month with 5 GB free.
Not given
Key features
Studio visual workflow builder with 1,000+ nodes
CLI and terminal-first interface
Ed25519-signed tamper-evident receipts
Human approval airlock for all actions
Autonomous agents that run 24/7
Capacity routing across organization machines
MCP integration with 33 tools
Multi-level human approval workflows
Offline receipt verification
Company-wide stop button
Connects to 15+ data systems including Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, Tableau, Looker, Power BI and Postgres
Extracts structural metadata including databases, schemas, tables, views, columns and relationship lineage
Provides Web-UI, MCP server and REST APIs for accessing semantic context
Automatic incremental updates with configurable sync schedules from on-demand to daily or weekly
Role-level access controls and data classifications including PII, PHI and SPII
Only reads metadata without accessing or moving actual data from systems
Integrates with quality monitoring tools including Monte Carlo, Anomalo and Bigeye
What makes it different
Entire local runtime free with MIT license, no login required
Cryptographically signed receipts verifiable offline
80% reduction in AI costs compared to top models
No execution counters or machine count limits
Eliminates confident AI mistakes by grounding agents in semantic context instead of having them guess from table names
Reduces AI token costs and speeds up answers by resolving schema and joins once upstream instead of in each agent query
Ensures consistent answers across all agents and teams using the same definitions and business logic
Provides complete traceability and auditability of AI decisions with documented context and ownership
RailCall
Kanlet